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Found 6 papers, 3 papers with code
Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning
In this paper, we introduce a novel deep learning framework, called Distributional Graphormer (DiG), in an attempt to predict the equilibrium distribution of molecular systems.
Exploring evolution-aware & -free protein language models as protein function predictors
Large-scale Protein Language Models (PLMs) have improved performance in protein prediction tasks, ranging from 3D structure prediction to various function predictions.
Co-evolution Transformer for Protein Contact Prediction
These methods generally derive coevolutionary features by aggregating the learned residue representations from individual sequences with equal weights, which is inconsistent with the premise that residue co-evolutions are a reflection of collective covariation patterns of numerous homologous proteins.
Pre-training Co-evolutionary Protein Representation via A Pairwise Masked Language Model
The key problem in the protein sequence representation learning is to capture the co-evolutionary information reflected by the inter-residue co-variation in the sequences.
Seq-SetNet: Exploring Sequence Sets for Inferring Structures
Therefore, the traditional deep neural networks designed for image processing cannot be directly applied on sequence sets.
Multiple Sequence Alignment
Protein Secondary Structure Prediction
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Predicting protein inter-residue contacts using composite likelihood maximization and deep learning
We further present successful application of the predicted contacts to accurately build tertiary structures for proteins in the PSICOV dataset.
